Humans and other advanced animals have an impressive capacity to recognize the behavioral significance, or category membership, of a wide range of sensory stimuli. This ability, which is disrupted by a number of brain diseases and conditions such as Alzheimer's disease, schizophrenia, stroke, and attention deficit disorder, is critical because it allows us to respond appropriately to the continuous stream of stimuli and events that we encounter in our interactions with the environment. Of course, we are not born with a built in library of meaningful categories, such as tables and chairs, which we are preprogrammed to recognize. Instead, we learn to recognize the meaning of such stimuli through experience. The goal of the studies proposed here is to move towards a more detailed understanding of the brain mechanisms underlying the learning and recognition visual categories. Recently, we found evidence that the posterior parietal cortex plays a surprisingly direct role in encoding the category membership of visual stimuli. In these studies, we recorded from neurons in the parietal cortex during performance of a categorization task in which 360 degrees of motion directions were grouped into two arbitrary categories that were divided by a learned category boundary. These recordings revealed that parietal neurons robustly encoded stimuli according to their learned category membership, suggesting that parietal visual representations can reflect abstract information about the learned significance of visual stimuli. The goals of the proposed studies are to develop a mechanistic understanding of how visual feature representations in visual cortex are transformed into category encoding in parietal cortex, and to determine how neuronal category signals develop in real time during the category learning process. While much is known about how the brain processes simple sensory features (such as color, orientation, and direction of motion), less is known about how the brain learns and represents the meaning, or category, of stimuli. A greater understanding of visual learning and categorization is critical for addressing a number of brain diseases and conditions (e.g. stroke, Alzheimer's disease, attention deficit disorder, schizophrenia, and stroke) that leave patients impaired in everyday tasks that require visual learning, recognition and/or evaluating and responding appropriately to sensory information. The long-term goal of this project is to guide the next generation of treatments for these brain-based diseases and disorders by helping to develop a detailed understanding of the brain mechanisms that underlie learning, memory and recognition. These studies also have relevance for understanding and addressing learning disabilities, such as attention deficit disorder and dyslexia, which affect a substantial fraction f school age children and young adults. Thus, a more detailed understanding of the basic brain mechanisms underlying learning and attention will likely give important insights into the causes and potential treatments for disorders involving these cognitive faculties.